A visual introduction to probability and statistics, covering basic probability, compound probability, probability distributions, frequentist inference, Bayesian inference, and regression analysis. Created by Daniel Kunin and team with interactive visualizations using D3.js.
Code Wiki is a platform that maintains a continuously updated, structured wiki for code repositories, aiming to improve developer productivity by unlocking knowledge buried in source code. It features automated documentation, intelligent context-aware chat, and integrated actionable links.
How a newly minted Radcliffe fellowship program helped Lily Macrakis, at the time a stay-at-home mother with a master’s degree, reignite her bookish passions—and reach her full scholarly potential.
The article details the life of Lily Macrakis, a historian of modern Greece, and how a newly established fellowship program at Radcliffe Institute in 1961 provided her with a "second chance" at intellectual life. As a stay-at-home mother with a master's degree, Macrakis felt her academic ambitions were stifled. The Radcliffe fellowship, designed to support talented women who had paused their careers due to family obligations, allowed her to pursue her research on Eleftherios Venizelos, complete her PhD, and eventually become a professor and department chair. The article highlights the innovative nature of the program, which was groundbreaking in its support of women balancing family and academic pursuits, and emphasizes how crucial the fellowship was to Macrakis’s successful career. It also touches on the societal challenges faced by women seeking intellectual fulfillment in the mid-20th century and the positive impact the program had on other fellows.
The article details the life of Lily Macrakis, a historian of modern Greece, and how a newly established fellowship program at Radcliffe Institute in 1961 provided her with a "second chance" at intellectual life. As a stay-at-home mother with a master's degree, Macrakis felt her academic ambitions were stifled. The Radcliffe fellowship, designed to support talented women who had paused their careers due to family obligations, allowed her to pursue her research on Eleftherios Venizelos, complete her PhD, and eventually become a professor and department chair. The article highlights the innovative nature of the program, which was groundbreaking in its support of women balancing family and academic pursuits, and emphasizes how crucial the fellowship was to Macrakis’s successful career. It also touches on the societal challenges faced by women seeking intellectual fulfillment in the mid-20th century and the positive impact the program had on other fellows.
This poster presents a computational model for narrative generation that incorporates Theory of Mind (ToM). It focuses on generating stories where characters have beliefs, desires, and intentions, and where these mental states influence their actions and the plot. The model uses a planning approach with a belief-desire-intention (BDI) architecture to represent character agency and generate coherent narratives. Key aspects include representing character knowledge, reasoning about others' beliefs, and generating actions based on these beliefs. The poster details the model's architecture, implementation, and preliminary evaluation.
The article advocates for NixOS as an excellent operating system for home labs, highlighting its declarative configuration approach, reproducibility, and immutability. It provides a step-by-step guide on installing NixOS in Proxmox, including addressing potential UEFI boot issues. It also explains how to configure and update NixOS, and discusses its strengths and weaknesses compared to other distributions. Finally, it introduces NixOS Anywhere as a tool for automated deployment.
Edison is a programmable robot designed to be a complete STEM teaching resource for coding and robotics education for students from 4 to 16 years of age. Edison empowers students to become not just coders, but inventors, problem solvers, and creative thinkers.
OpenAI releases GPT-5.1 Instant and GPT-5.1 Thinking, upgrades to the GPT-5 series focusing on improved intelligence, conversational style, and customization options for ChatGPT. Includes new tone presets and the ability to fine-tune characteristics.
Booting and auto provisioning a Docker Swarm cluster with PXE and enjoying zero-downtime deployments from a single git repository
The Rust version of sudo (sudo-rs) used in Ubuntu 25.10 has two moderate security vulnerabilities. Updates are being released to address these issues, including preventing password leaks and improving feedback handling.
LLMs are powerful for understanding user input and generating human‑like text, but they are not reliable arbiters of logic. A production‑grade system should:
- Isolate the LLM to language tasks only.
- Put all business rules and tool orchestration in deterministic code.
- Validate every step with automated tests and logging.
- Prefer local models for sensitive domains like healthcare.
| **Issue** | **What users observed** | **Common solutions** |
|-----------|------------------------|----------------------|
| **Hallucinations & false assumptions** | LLMs often answer without calling the required tool, e.g., claiming a doctor is unavailable when the calendar shows otherwise. | Move decision‑making out of the model. Let the code decide and use the LLM only for phrasing or clarification. |
| **Inconsistent tool usage** | Models agree to user requests, then later report the opposite (e.g., confirming an appointment but actually scheduling none). | Enforce deterministic tool calls first, then let the LLM format the result. Use “always‑call‑tool‑first” guards in the prompt. |
| **Privacy concerns** | Sending patient data to cloud APIs is risky. | Prefer self‑hosted/local models (e.g., LLaMA, Qwen) or keep all data on‑premises. |
| **Prompt brittleness** | Adding more rules can make prompts unstable; models still improvise. | Keep prompts short, give concrete examples, and test with a structured evaluation pipeline. |
| **Evaluation & monitoring** | Without systematic “evals,” failures go unnoticed. | Build automated test suites (e.g., with LangChain, LangGraph, or custom eval scripts) that verify correct tool calls and output formats. |
| **Workflow design** | Treat the LLM as a *translator* rather than a *decision engine*. | • Extract intent → produce a JSON/action spec → execute deterministic code → have the LLM produce a user‑friendly response. <br>• Cache common replies to avoid unnecessary model calls. |
| **Alternative UI** | Many suggest a simple button‑driven interface for scheduling. | Use the LLM only for natural‑language front‑end; the back‑end remains a conventional, rule‑based system. |
- Isolate the LLM to language tasks only.
- Put all business rules and tool orchestration in deterministic code.
- Validate every step with automated tests and logging.
- Prefer local models for sensitive domains like healthcare.
| **Issue** | **What users observed** | **Common solutions** |
|-----------|------------------------|----------------------|
| **Hallucinations & false assumptions** | LLMs often answer without calling the required tool, e.g., claiming a doctor is unavailable when the calendar shows otherwise. | Move decision‑making out of the model. Let the code decide and use the LLM only for phrasing or clarification. |
| **Inconsistent tool usage** | Models agree to user requests, then later report the opposite (e.g., confirming an appointment but actually scheduling none). | Enforce deterministic tool calls first, then let the LLM format the result. Use “always‑call‑tool‑first” guards in the prompt. |
| **Privacy concerns** | Sending patient data to cloud APIs is risky. | Prefer self‑hosted/local models (e.g., LLaMA, Qwen) or keep all data on‑premises. |
| **Prompt brittleness** | Adding more rules can make prompts unstable; models still improvise. | Keep prompts short, give concrete examples, and test with a structured evaluation pipeline. |
| **Evaluation & monitoring** | Without systematic “evals,” failures go unnoticed. | Build automated test suites (e.g., with LangChain, LangGraph, or custom eval scripts) that verify correct tool calls and output formats. |
| **Workflow design** | Treat the LLM as a *translator* rather than a *decision engine*. | • Extract intent → produce a JSON/action spec → execute deterministic code → have the LLM produce a user‑friendly response. <br>• Cache common replies to avoid unnecessary model calls. |
| **Alternative UI** | Many suggest a simple button‑driven interface for scheduling. | Use the LLM only for natural‑language front‑end; the back‑end remains a conventional, rule‑based system. |
